• Dodd, James
• Mirmehdi, Majid
• Soleimani, Vahid
• Sharp, Charles
• Damen, Dima
• Hannuna, Sion
• Camplani, Massimo

Abstract

Objective: We  propose a novel depth-based Photoplethysmography (dPPG) approach to reduce motion artifacts in respiratory volume–time data and improve the accuracy of remote pulmonary function testing (PFT) measures.<br/><br/>Method: Following spatial and temporal calibration of two opposing RGB-D sensors, a dynamic 3-D model of the subject performing PFT is reconstructed and used to decouple trunk movements from respiratory motions. Depth-based volume–time data is then retrieved, calibrated and used to compute 11 clinical PFT measures for forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests.<br/><br/>Results: A dataset of 35 subjects (298 sequences) was collected and used to evaluate the proposed dPPG method by comparing depth-based PFT measures to the measures provided by a spirometer. Other comparative experiments between the dPPG and the single Kinect approach, such as Bland-Altman analysis, similarity measures performance, intra-subject error analysis, and statistical analysis of tidal volume and main effort scaling factors, all show the superior accuracy of the dPPG approach.<br/><br/>Conclusion: We introduce a depth-based whole body photoplethysmography approach which reduces motion artifacts in depth-based volume–time data and highly improves the accuracy of depth-based computed measures.<br/><br/>Significance: The proposed dPPG method remarkably drops the L2error mean and standard deviation of FEF50% , FEF75% , FEF25-75% , IC, and ERV measures by half, compared to the single Kinect approach. These significant improvements establish the potential for unconstrained remote respiratory monitoring and diagnosis.

Topics

• impedance spectroscopy
• experiment
• ion chromatography